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In this chapter, we propose a MFG-guided deep deterministic policy gradient (DDPG) for the task placement in the cooperative MEC, which can help servers make timely task placement decisions, and significantly reduce average service delay. Instead of applying MFG or DRL separately, we jointly leverage MFG and DRL for task placement, and let the equilibrium of MFG guide the learning directions of DRL. We also ensure that the MFG and DRL approaches are consistent with the same goal.

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Mean Field Game Guided Deep Reinforcement Learning

  • 章节

  • 发表日期：

    2025年

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  • Yuhan Kang,
  • Hao Gao,
  • Zhu Han

摘要

In this chapter, we propose a MFG-guided deep deterministic policy gradient (DDPG) for the task placement in the cooperative MEC, which can help servers make timely task placement decisions, and significantly reduce average service delay. Instead of applying MFG or DRL separately, we jointly leverage MFG and DRL for task placement, and let the equilibrium of MFG guide the learning directions of DRL. We also ensure that the MFG and DRL approaches are consistent with the same goal.